Using ODP metadata to personalize search

TLDR

The Open Directory Project is a large collaborative effort that has annotated more than 4 million web pages with topic and importance metadata. This study investigates whether the ODP metadata can be leveraged to improve personalized web search. The authors introduce a ranking criterion based on the distance between a user profile defined by ODP topics and the ODP topics of each URL, and then examine how biasing PageRank on ODP subtopics can extend these metadata to the broader web. Empirical results show that this ODP‑based ranking yields better search outcomes than standard Google search.

Abstract

The Open Directory Project is clearly one of the largest collaborative efforts to manually annotate web pages. This effort involves over 65,000 editors and resulted in metadata specifying topic and importance for more than 4 million web pages. Still, given that this number is just about 0.05 percent of the Web pages indexed by Google, is this effort enough to make a difference? In this paper we discuss how these metadata can be exploited to achieve high quality personalized web search. First, we address this by introducing an additional criterion for web page ranking, namely the distance between a user profile defined using ODP topics and the sets of ODP topics covered by each URL returned in regular web search. We empirically show that this enhancement yields better results than current web search using Google. Then, in the second part of the paper, we investigate the boundaries of biasing PageRank on subtopics of the ODP in order to automatically extend these metadata to the whole web.